Overview

Dataset statistics

Number of variables15
Number of observations5762
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory720.2 KiB
Average record size in memory128.0 B

Variable types

Numeric15

Alerts

revenue_purc is highly overall correlated with volume_products_purc and 6 other fieldsHigh correlation
recency_days is highly overall correlated with volume_products_purc and 3 other fieldsHigh correlation
volume_products_purc is highly overall correlated with revenue_purc and 7 other fieldsHigh correlation
assort_products_purc is highly overall correlated with revenue_purc and 5 other fieldsHigh correlation
purchases is highly overall correlated with revenue_purc and 6 other fieldsHigh correlation
avg_period_purc is highly overall correlated with revenue_purc and 5 other fieldsHigh correlation
frequency_purc is highly overall correlated with recency_days and 2 other fieldsHigh correlation
volume_basket_size_purc is highly overall correlated with revenue_purc and 5 other fieldsHigh correlation
assort_basket_size_purc is highly overall correlated with assort_products_purc and 2 other fieldsHigh correlation
avg_ticket_purc is highly overall correlated with revenue_purc and 5 other fieldsHigh correlation
returns is highly overall correlated with purchases and 2 other fieldsHigh correlation
volume_products_ret is highly overall correlated with returns and 1 other fieldsHigh correlation
revenue_ret is highly overall correlated with returns and 1 other fieldsHigh correlation
revenue_real is highly overall correlated with revenue_purc and 6 other fieldsHigh correlation
revenue_purc is highly skewed (γ1 = 21.76985821)Skewed
volume_products_purc is highly skewed (γ1 = 23.18016801)Skewed
volume_basket_size_purc is highly skewed (γ1 = 48.78459009)Skewed
avg_ticket_purc is highly skewed (γ1 = 27.95618163)Skewed
volume_products_ret is highly skewed (γ1 = 51.81822874)Skewed
revenue_ret is highly skewed (γ1 = 59.82311707)Skewed
revenue_real is highly skewed (γ1 = 23.87344331)Skewed
customer_id has unique valuesUnique
revenue_purc has 67 (1.2%) zerosZeros
volume_products_purc has 67 (1.2%) zerosZeros
assort_products_purc has 67 (1.2%) zerosZeros
purchases has 67 (1.2%) zerosZeros
frequency_purc has 67 (1.2%) zerosZeros
volume_basket_size_purc has 67 (1.2%) zerosZeros
assort_basket_size_purc has 67 (1.2%) zerosZeros
avg_ticket_purc has 67 (1.2%) zerosZeros
returns has 4190 (72.7%) zerosZeros
volume_products_ret has 4190 (72.7%) zerosZeros
revenue_ret has 4190 (72.7%) zerosZeros

Reproduction

Analysis started2023-05-15 20:45:44.467849
Analysis finished2023-05-15 20:46:41.533147
Duration57.07 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct5762
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16622.832
Minimum12346
Maximum22709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-05-15T17:46:41.964945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12701.05
Q114298.25
median16251
Q318235.75
95-th percentile21768.95
Maximum22709
Range10363
Interquartile range (IQR)3937.5

Descriptive statistics

Standard deviation2817.1868
Coefficient of variation (CV)0.16947694
Kurtosis-0.83876473
Mean16622.832
Median Absolute Deviation (MAD)1970.5
Skewness0.43163523
Sum95780760
Variance7936541.6
MonotonicityNot monotonic
2023-05-15T17:46:42.295753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
21088 1
 
< 0.1%
21086 1
 
< 0.1%
15578 1
 
< 0.1%
12424 1
 
< 0.1%
21084 1
 
< 0.1%
17837 1
 
< 0.1%
21081 1
 
< 0.1%
14327 1
 
< 0.1%
21078 1
 
< 0.1%
Other values (5752) 5752
99.8%
ValueCountFrequency (%)
12346 1
< 0.1%
12347 1
< 0.1%
12348 1
< 0.1%
12349 1
< 0.1%
12350 1
< 0.1%
12352 1
< 0.1%
12353 1
< 0.1%
12354 1
< 0.1%
12355 1
< 0.1%
12356 1
< 0.1%
ValueCountFrequency (%)
22709 1
< 0.1%
22708 1
< 0.1%
22707 1
< 0.1%
22706 1
< 0.1%
22705 1
< 0.1%
22704 1
< 0.1%
22700 1
< 0.1%
22699 1
< 0.1%
22696 1
< 0.1%
22695 1
< 0.1%

revenue_purc
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct5450
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1781.5213
Minimum0
Maximum279138.02
Zeros67
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-05-15T17:46:42.635562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.855
Q1225.6275
median602.03
Q31554.8475
95-th percentile5234.605
Maximum279138.02
Range279138.02
Interquartile range (IQR)1329.22

Descriptive statistics

Standard deviation7839.4074
Coefficient of variation (CV)4.4004006
Kurtosis617.23663
Mean1781.5213
Median Absolute Deviation (MAD)479.44
Skewness21.769858
Sum10265126
Variance61456308
MonotonicityNot monotonic
2023-05-15T17:46:42.919395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67
 
1.2%
7.95 9
 
0.2%
4.95 8
 
0.1%
2.95 8
 
0.1%
1.25 8
 
0.1%
3.75 7
 
0.1%
12.75 7
 
0.1%
1.65 7
 
0.1%
7.5 6
 
0.1%
4.25 6
 
0.1%
Other values (5440) 5629
97.7%
ValueCountFrequency (%)
0 67
1.2%
0.42 1
 
< 0.1%
0.65 1
 
< 0.1%
0.79 1
 
< 0.1%
0.84 4
 
0.1%
0.85 3
 
0.1%
1.07 1
 
< 0.1%
1.25 8
 
0.1%
1.44 1
 
< 0.1%
1.65 7
 
0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
136275.72 1
< 0.1%
124564.53 1
< 0.1%
116729.63 1
< 0.1%
91062.38 1
< 0.1%
77183.6 1
< 0.1%
72882.09 1
< 0.1%

recency_days
Real number (ℝ)

Distinct305
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.19872
Minimum0
Maximum400
Zeros38
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-05-15T17:46:43.318170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123
median72
Q3206
95-th percentile354
Maximum400
Range400
Interquartile range (IQR)183

Descriptive statistics

Standard deviation115.0544
Coefficient of variation (CV)0.9572016
Kurtosis-0.60870472
Mean120.19872
Median Absolute Deviation (MAD)63
Skewness0.82223292
Sum692585
Variance13237.516
MonotonicityNot monotonic
2023-05-15T17:46:43.653976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 110
 
1.9%
4 105
 
1.8%
3 98
 
1.7%
2 91
 
1.6%
10 86
 
1.5%
8 82
 
1.4%
9 79
 
1.4%
17 79
 
1.4%
7 78
 
1.4%
15 67
 
1.2%
Other values (295) 4887
84.8%
ValueCountFrequency (%)
0 38
 
0.7%
1 110
1.9%
2 91
1.6%
3 98
1.7%
4 105
1.8%
5 52
0.9%
7 78
1.4%
8 82
1.4%
9 79
1.4%
10 86
1.5%
ValueCountFrequency (%)
400 67
1.2%
373 23
 
0.4%
372 22
 
0.4%
371 17
 
0.3%
369 4
 
0.1%
368 13
 
0.2%
367 16
 
0.3%
366 15
 
0.3%
365 19
 
0.3%
364 11
 
0.2%

volume_products_purc
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1844
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean967.23464
Minimum0
Maximum196844
Zeros67
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-05-15T17:46:43.956803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q1101
median310
Q3799
95-th percentile2905.65
Maximum196844
Range196844
Interquartile range (IQR)698

Descriptive statistics

Standard deviation4404.1613
Coefficient of variation (CV)4.5533536
Kurtosis794.08986
Mean967.23464
Median Absolute Deviation (MAD)252
Skewness23.180168
Sum5573206
Variance19396637
MonotonicityNot monotonic
2023-05-15T17:46:44.224669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 114
 
2.0%
2 72
 
1.2%
0 67
 
1.2%
3 51
 
0.9%
4 49
 
0.9%
5 35
 
0.6%
6 29
 
0.5%
12 25
 
0.4%
88 22
 
0.4%
72 21
 
0.4%
Other values (1834) 5277
91.6%
ValueCountFrequency (%)
0 67
1.2%
1 114
2.0%
2 72
1.2%
3 51
0.9%
4 49
0.9%
5 35
 
0.6%
6 29
 
0.5%
7 20
 
0.3%
8 18
 
0.3%
9 7
 
0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80997 1
< 0.1%
80179 1
< 0.1%
77373 1
< 0.1%
74215 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%

assort_products_purc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct437
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.844846
Minimum0
Maximum1785
Zeros67
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-05-15T17:46:44.911086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q113
median35
Q383
95-th percentile240
Maximum1785
Range1785
Interquartile range (IQR)70

Descriptive statistics

Standard deviation101.39387
Coefficient of variation (CV)1.4727882
Kurtosis44.057421
Mean68.844846
Median Absolute Deviation (MAD)27
Skewness4.7120918
Sum396684
Variance10280.717
MonotonicityNot monotonic
2023-05-15T17:46:45.155966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 279
 
4.8%
2 148
 
2.6%
3 114
 
2.0%
10 101
 
1.8%
5 99
 
1.7%
9 96
 
1.7%
8 92
 
1.6%
6 92
 
1.6%
11 92
 
1.6%
13 91
 
1.6%
Other values (427) 4558
79.1%
ValueCountFrequency (%)
0 67
 
1.2%
1 279
4.8%
2 148
2.6%
3 114
2.0%
4 89
 
1.5%
5 99
 
1.7%
6 92
 
1.6%
7 91
 
1.6%
8 92
 
1.6%
9 96
 
1.7%
ValueCountFrequency (%)
1785 1
< 0.1%
1766 1
< 0.1%
1322 1
< 0.1%
1118 1
< 0.1%
1108 1
< 0.1%
884 1
< 0.1%
816 1
< 0.1%
748 1
< 0.1%
730 1
< 0.1%
720 1
< 0.1%

purchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4288442
Minimum0
Maximum206
Zeros67
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-05-15T17:46:45.500746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q34
95-th percentile11
Maximum206
Range206
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.773486
Coefficient of variation (CV)1.9754429
Kurtosis303.3001
Mean3.4288442
Median Absolute Deviation (MAD)1
Skewness13.204776
Sum19757
Variance45.880112
MonotonicityNot monotonic
2023-05-15T17:46:45.797599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2871
49.8%
2 827
 
14.4%
3 501
 
8.7%
4 395
 
6.9%
5 236
 
4.1%
6 173
 
3.0%
7 139
 
2.4%
8 98
 
1.7%
9 69
 
1.2%
0 67
 
1.2%
Other values (48) 386
 
6.7%
ValueCountFrequency (%)
0 67
 
1.2%
1 2871
49.8%
2 827
 
14.4%
3 501
 
8.7%
4 395
 
6.9%
5 236
 
4.1%
6 173
 
3.0%
7 139
 
2.4%
8 98
 
1.7%
9 69
 
1.2%
ValueCountFrequency (%)
206 1
< 0.1%
198 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 1
< 0.1%
90 1
< 0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
< 0.1%
60 1
< 0.1%

avg_period_purc
Real number (ℝ)

Distinct1155
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.48757
Minimum1
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-05-15T17:46:46.037709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q161.3125
median400
Q3400
95-th percentile400
Maximum400
Range399
Interquartile range (IQR)338.6875

Descriptive statistics

Standard deviation166.98758
Coefficient of variation (CV)0.68022827
Kurtosis-1.8272988
Mean245.48757
Median Absolute Deviation (MAD)0
Skewness-0.24031186
Sum1414499.4
Variance27884.853
MonotonicityNot monotonic
2023-05-15T17:46:46.302577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 2990
51.9%
70 21
 
0.4%
46 18
 
0.3%
55 17
 
0.3%
49 16
 
0.3%
31 16
 
0.3%
91 16
 
0.3%
21 15
 
0.3%
35 15
 
0.3%
42 15
 
0.3%
Other values (1145) 2623
45.5%
ValueCountFrequency (%)
1 9
0.2%
2 4
0.1%
2.88372093 1
 
< 0.1%
3 6
0.1%
3.330357143 1
 
< 0.1%
3.351351351 1
 
< 0.1%
4 4
0.1%
4.191011236 1
 
< 0.1%
4.275862069 1
 
< 0.1%
4.5 1
 
< 0.1%
ValueCountFrequency (%)
400 2990
51.9%
366 1
 
< 0.1%
365 1
 
< 0.1%
364 1
 
< 0.1%
363 1
 
< 0.1%
357 2
 
< 0.1%
356 1
 
< 0.1%
355 2
 
< 0.1%
352 1
 
< 0.1%
351 2
 
< 0.1%

frequency_purc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1223
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.022794603
Minimum0
Maximum1
Zeros67
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-05-15T17:46:46.569425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0028248588
Q10.0054054054
median0.011764706
Q30.023465009
95-th percentile0.068181818
Maximum1
Range1
Interquartile range (IQR)0.018059603

Descriptive statistics

Standard deviation0.048059033
Coefficient of variation (CV)2.1083514
Kurtosis174.74357
Mean0.022794603
Median Absolute Deviation (MAD)0.0075093867
Skewness10.833933
Sum131.3425
Variance0.0023096707
MonotonicityNot monotonic
2023-05-15T17:46:46.816284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67
 
1.2%
0.01851851852 37
 
0.6%
0.005405405405 32
 
0.6%
0.01639344262 31
 
0.5%
0.002824858757 30
 
0.5%
0.004672897196 30
 
0.5%
0.01538461538 29
 
0.5%
0.05263157895 29
 
0.5%
0.01923076923 28
 
0.5%
0.025 27
 
0.5%
Other values (1213) 5422
94.1%
ValueCountFrequency (%)
0 67
1.2%
0.002673796791 22
 
0.4%
0.002680965147 21
 
0.4%
0.002688172043 17
 
0.3%
0.002702702703 3
 
0.1%
0.0027100271 13
 
0.2%
0.002717391304 16
 
0.3%
0.00272479564 14
 
0.2%
0.002732240437 19
 
0.3%
0.002739726027 11
 
0.2%
ValueCountFrequency (%)
1 5
0.1%
0.550802139 1
 
< 0.1%
0.5294117647 1
 
< 0.1%
0.5 11
0.2%
0.4 1
 
< 0.1%
0.3333333333 6
0.1%
0.3315508021 1
 
< 0.1%
0.3157894737 1
 
< 0.1%
0.2727272727 2
 
< 0.1%
0.2621621622 1
 
< 0.1%

volume_basket_size_purc
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2367
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265.16374
Minimum0
Maximum74215
Zeros67
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-05-15T17:46:47.056125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q172.541667
median149.33333
Q3288
95-th percentile732
Maximum74215
Range74215
Interquartile range (IQR)215.45833

Descriptive statistics

Standard deviation1192.5611
Coefficient of variation (CV)4.4974515
Kurtosis2798.1125
Mean265.16374
Median Absolute Deviation (MAD)97.25
Skewness48.78459
Sum1527873.5
Variance1422201.9
MonotonicityNot monotonic
2023-05-15T17:46:47.289991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 115
 
2.0%
2 71
 
1.2%
0 67
 
1.2%
3 51
 
0.9%
4 49
 
0.9%
5 35
 
0.6%
6 29
 
0.5%
12 26
 
0.5%
100 22
 
0.4%
72 22
 
0.4%
Other values (2357) 5275
91.5%
ValueCountFrequency (%)
0 67
1.2%
1 115
2.0%
2 71
1.2%
3 51
0.9%
3.333333333 1
 
< 0.1%
4 49
0.9%
5 35
 
0.6%
5.333333333 1
 
< 0.1%
5.666666667 1
 
< 0.1%
6 29
 
0.5%
ValueCountFrequency (%)
74215 1
< 0.1%
40498.5 1
< 0.1%
14149 1
< 0.1%
13956 1
< 0.1%
7824 1
< 0.1%
6009.333333 1
< 0.1%
5963 1
< 0.1%
5196 1
< 0.1%
4300 1
< 0.1%
4282 1
< 0.1%

assort_basket_size_purc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1179
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.817243
Minimum0
Maximum1108
Zeros67
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-05-15T17:46:47.552862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median15
Q330.666667
95-th percentile172
Maximum1108
Range1108
Interquartile range (IQR)23.666667

Descriptive statistics

Standard deviation76.518212
Coefficient of variation (CV)2.0783254
Kurtosis33.222841
Mean36.817243
Median Absolute Deviation (MAD)10
Skewness5.0981723
Sum212140.96
Variance5855.0368
MonotonicityNot monotonic
2023-05-15T17:46:47.793725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 278
 
4.8%
2 160
 
2.8%
3 115
 
2.0%
9 106
 
1.8%
10 105
 
1.8%
7 103
 
1.8%
5 102
 
1.8%
8 102
 
1.8%
6 101
 
1.8%
13 99
 
1.7%
Other values (1169) 4491
77.9%
ValueCountFrequency (%)
0 67
1.2%
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 7
 
0.1%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 12
 
0.2%
0.5454545455 1
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
ValueCountFrequency (%)
1108 1
< 0.1%
748 1
< 0.1%
730 1
< 0.1%
720 1
< 0.1%
703 1
< 0.1%
686 1
< 0.1%
674 1
< 0.1%
673 1
< 0.1%
660 1
< 0.1%
649 1
< 0.1%

avg_ticket_purc
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct5455
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean575.05808
Minimum0
Maximum84236.25
Zeros67
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-05-15T17:46:48.034567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.855
Q1155.2725
median292.11937
Q3481.625
95-th percentile1830.902
Maximum84236.25
Range84236.25
Interquartile range (IQR)326.3525

Descriptive statistics

Standard deviation2029.7879
Coefficient of variation (CV)3.5297093
Kurtosis998.0428
Mean575.05808
Median Absolute Deviation (MAD)152.29625
Skewness27.956182
Sum3313484.7
Variance4120038.8
MonotonicityNot monotonic
2023-05-15T17:46:48.270435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67
 
1.2%
7.95 9
 
0.2%
2.95 8
 
0.1%
1.25 8
 
0.1%
4.95 8
 
0.1%
1.65 7
 
0.1%
12.75 7
 
0.1%
3.75 7
 
0.1%
4.25 6
 
0.1%
5.95 6
 
0.1%
Other values (5445) 5629
97.7%
ValueCountFrequency (%)
0 67
1.2%
0.42 1
 
< 0.1%
0.65 1
 
< 0.1%
0.79 1
 
< 0.1%
0.84 4
 
0.1%
0.85 3
 
0.1%
1.07 1
 
< 0.1%
1.25 8
 
0.1%
1.44 1
 
< 0.1%
1.65 7
 
0.1%
ValueCountFrequency (%)
84236.25 1
< 0.1%
77183.6 1
< 0.1%
52940.94 1
< 0.1%
50653.91 1
< 0.1%
21389.6 1
< 0.1%
18745.86 1
< 0.1%
14844.76667 1
< 0.1%
14838.86 1
< 0.1%
13305.5 1
< 0.1%
12681.58 1
< 0.1%

returns
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59371746
Minimum0
Maximum45
Zeros4190
Zeros (%)72.7%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-05-15T17:46:48.481312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum45
Range45
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7411912
Coefficient of variation (CV)2.9326932
Kurtosis192.19013
Mean0.59371746
Median Absolute Deviation (MAD)0
Skewness10.302776
Sum3421
Variance3.0317467
MonotonicityNot monotonic
2023-05-15T17:46:48.667225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 4190
72.7%
1 914
 
15.9%
2 292
 
5.1%
3 140
 
2.4%
4 92
 
1.6%
5 37
 
0.6%
6 32
 
0.6%
7 21
 
0.4%
9 8
 
0.1%
11 5
 
0.1%
Other values (13) 31
 
0.5%
ValueCountFrequency (%)
0 4190
72.7%
1 914
 
15.9%
2 292
 
5.1%
3 140
 
2.4%
4 92
 
1.6%
5 37
 
0.6%
6 32
 
0.6%
7 21
 
0.4%
8 5
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
45 1
 
< 0.1%
44 1
 
< 0.1%
35 1
 
< 0.1%
27 1
 
< 0.1%
21 1
 
< 0.1%
18 2
 
< 0.1%
17 1
 
< 0.1%
15 2
 
< 0.1%
14 1
 
< 0.1%
13 5
0.1%

volume_products_ret
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct222
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.210691
Minimum0
Maximum80995
Zeros4190
Zeros (%)72.7%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-05-15T17:46:48.891077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile40
Maximum80995
Range80995
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1466.2339
Coefficient of variation (CV)31.057243
Kurtosis2749.5552
Mean47.210691
Median Absolute Deviation (MAD)0
Skewness51.818229
Sum272028
Variance2149841.8
MonotonicityNot monotonic
2023-05-15T17:46:49.138956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4190
72.7%
1 192
 
3.3%
2 157
 
2.7%
3 106
 
1.8%
4 93
 
1.6%
6 80
 
1.4%
5 64
 
1.1%
12 56
 
1.0%
8 44
 
0.8%
7 44
 
0.8%
Other values (212) 736
 
12.8%
ValueCountFrequency (%)
0 4190
72.7%
1 192
 
3.3%
2 157
 
2.7%
3 106
 
1.8%
4 93
 
1.6%
5 64
 
1.1%
6 80
 
1.4%
7 44
 
0.8%
8 44
 
0.8%
9 42
 
0.7%
ValueCountFrequency (%)
80995 1
< 0.1%
74215 1
< 0.1%
9360 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3331 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%

revenue_ret
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1121
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.081513
Minimum0
Maximum168469.6
Zeros4190
Zeros (%)72.7%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-05-15T17:46:49.411800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.95
95-th percentile114.9965
Maximum168469.6
Range168469.6
Interquartile range (IQR)4.95

Descriptive statistics

Standard deviation2479.1566
Coefficient of variation (CV)29.840051
Kurtosis3859.0771
Mean83.081513
Median Absolute Deviation (MAD)0
Skewness59.823117
Sum478715.68
Variance6146217.6
MonotonicityNot monotonic
2023-05-15T17:46:49.656659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4190
72.7%
12.75 21
 
0.4%
4.95 20
 
0.3%
15 19
 
0.3%
9.95 17
 
0.3%
5.9 13
 
0.2%
25.5 11
 
0.2%
3.75 10
 
0.2%
4.25 10
 
0.2%
7.5 9
 
0.2%
Other values (1111) 1442
 
25.0%
ValueCountFrequency (%)
0 4190
72.7%
0.42 2
 
< 0.1%
0.65 1
 
< 0.1%
0.95 1
 
< 0.1%
1.25 6
 
0.1%
1.45 4
 
0.1%
1.64 1
 
< 0.1%
1.65 5
 
0.1%
1.7 2
 
< 0.1%
1.79 1
 
< 0.1%
ValueCountFrequency (%)
168469.6 1
< 0.1%
77183.6 1
< 0.1%
22998.4 1
< 0.1%
14688.24 1
< 0.1%
8511.15 1
< 0.1%
7393.59 1
< 0.1%
5228.4 1
< 0.1%
4815.26 1
< 0.1%
4814.74 1
< 0.1%
4486.24 1
< 0.1%

revenue_real
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5510
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1698.4398
Minimum-1208.04
Maximum278778.02
Zeros10
Zeros (%)0.2%
Negative69
Negative (%)1.2%
Memory size90.0 KiB
2023-05-15T17:46:49.918487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1208.04
5-th percentile8.2525
Q1218.6975
median593.875
Q31533.5025
95-th percentile5150.0125
Maximum278778.02
Range279986.06
Interquartile range (IQR)1314.805

Descriptive statistics

Standard deviation7311.1671
Coefficient of variation (CV)4.3046373
Kurtosis751.13488
Mean1698.4398
Median Absolute Deviation (MAD)474.415
Skewness23.873443
Sum9786409.9
Variance53453164
MonotonicityNot monotonic
2023-05-15T17:46:50.145379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
0.2%
7.95 9
 
0.2%
2.95 8
 
0.1%
1.25 8
 
0.1%
4.95 8
 
0.1%
12.75 7
 
0.1%
3.75 7
 
0.1%
1.65 7
 
0.1%
4.25 6
 
0.1%
5.95 6
 
0.1%
Other values (5500) 5686
98.7%
ValueCountFrequency (%)
-1208.04 1
< 0.1%
-1192.2 1
< 0.1%
-1081.14 1
< 0.1%
-811.86 1
< 0.1%
-572 1
< 0.1%
-464.9 1
< 0.1%
-313.86 1
< 0.1%
-295.09 1
< 0.1%
-288 1
< 0.1%
-227.44 1
< 0.1%
ValueCountFrequency (%)
278778.02 1
< 0.1%
259657.3 1
< 0.1%
189735.53 1
< 0.1%
128882.13 1
< 0.1%
123638.18 1
< 0.1%
113855.32 1
< 0.1%
88138.2 1
< 0.1%
65920.12 1
< 0.1%
62924.1 1
< 0.1%
59419.34 1
< 0.1%

Interactions

2023-05-15T17:46:36.801469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:46.382470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:50.827926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:54.961845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:58.595227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:02.069894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:05.373260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:08.720343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:12.712911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:15.907902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:19.288228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:22.485381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:26.498209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:29.712595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:33.182046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:37.012353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:47.362887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:51.029793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:55.185717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:58.824118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:02.293761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:05.571146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:09.021173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:12.912800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:16.125777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:19.494090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:22.690263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:26.690121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:29.927467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:33.397922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:37.228224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:47.591777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:51.227679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:55.404613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:59.043972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:02.510636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:05.762037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:09.291577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:13.112684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:16.356887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:19.692976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:22.892147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:26.888989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:30.152343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:33.619035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:37.451095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:47.907598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:51.453549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:55.640456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:59.317815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:02.743504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:05.975919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:09.595401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:13.349570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:16.598745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:19.917846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:23.109020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:27.110861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:30.387208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:33.851900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:37.680963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:48.185436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:51.689415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:55.892313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:59.577670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:02.981366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:06.204785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:09.929212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:13.578417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:16.840612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:20.150714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:23.339891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:27.335733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:30.652055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:34.086770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:37.908830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:48.406294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:51.965253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:56.121186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:59.810535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:03.199242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:06.417669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:10.494886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:13.799295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:17.070476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:20.369589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:23.553769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:27.552606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:30.893913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:34.329629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:38.108938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:48.622190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:52.295068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:56.332060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:00.022414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:03.397132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:06.601555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:10.701768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:14.003173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:17.280381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:20.566475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:23.739663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:27.757487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:31.101019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:34.539761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:38.336809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:48.916003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:52.668854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:56.566929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:00.264271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:03.632996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:06.823431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:10.933636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:14.233063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:17.519219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:20.791368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:23.984520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:28.017339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:31.389872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:34.860575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:38.547686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:49.176871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:52.987669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:56.791798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:00.487507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:03.843879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:07.019316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:11.154509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:14.441922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:17.732101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:20.997232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:24.251366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:28.247211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:31.613745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:35.131424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:38.776556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:49.423707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:53.426961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:57.024671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:00.736372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:04.068748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:07.240190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:11.389396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:14.667377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:17.958971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:21.220101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:24.560191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:28.466086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:31.846592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:35.434251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:38.973445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:49.705551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:53.711360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:57.238562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:00.961239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:04.288624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:07.442096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:11.605253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:14.868247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:18.172845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:21.423990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:24.840033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:28.694957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:32.070482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:35.710090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:39.176331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:49.908430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:53.991200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:57.446422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:01.180112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:04.488526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:07.635985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:11.817445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:15.059134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:18.380750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:21.625869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:25.472800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:28.896838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:32.276348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:35.912976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:39.376215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:50.099320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:54.295337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:57.648307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:01.386992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:04.697385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:07.821857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:12.024323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:15.253027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:18.594604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:21.822757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:25.719654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:29.083735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:32.484224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:36.120856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:39.608080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:50.395154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:54.545085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:57.892170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:01.627145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:04.930255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:08.119690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:12.265170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:15.482913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:18.834469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:22.051647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:26.060459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:29.302606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:32.722309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:36.358719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:39.832953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:50.620044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:54.767958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:45:58.369377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:01.862016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:05.155125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:08.431508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:12.492038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:15.705784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:19.067337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:22.286493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:26.292328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:29.515485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:32.957178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-15T17:46:36.586591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-15T17:46:50.399814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
customer_idrevenue_purcrecency_daysvolume_products_purcassort_products_purcpurchasesavg_period_purcfrequency_purcvolume_basket_size_purcassort_basket_size_purcavg_ticket_purcreturnsvolume_products_retrevenue_retrevenue_real
customer_id1.000-0.1890.251-0.296-0.029-0.3860.383-0.221-0.1550.119-0.051-0.259-0.261-0.259-0.181
revenue_purc-0.1891.000-0.4460.9320.8010.657-0.5760.4230.7320.3910.7730.4000.3910.3910.994
recency_days0.251-0.4461.000-0.514-0.349-0.6110.548-0.876-0.2260.014-0.117-0.293-0.281-0.280-0.445
volume_products_purc-0.2960.932-0.5141.0000.7430.705-0.6280.4900.7970.3220.6630.4190.4140.4080.926
assort_products_purc-0.0290.801-0.3490.7431.0000.470-0.3930.3160.6310.7420.6520.2440.2320.2310.806
purchases-0.3860.657-0.6110.7050.4701.000-0.9210.5620.188-0.1360.1110.5040.4890.4890.654
avg_period_purc0.383-0.5760.548-0.628-0.393-0.9211.000-0.570-0.1570.157-0.079-0.465-0.453-0.453-0.572
frequency_purc-0.2210.423-0.8760.4900.3160.562-0.5701.0000.222-0.0350.1120.2840.2720.2720.422
volume_basket_size_purc-0.1550.732-0.2260.7970.6310.188-0.1570.2221.0000.5850.8590.1410.1470.1390.728
assort_basket_size_purc0.1190.3910.0140.3220.742-0.1360.157-0.0350.5851.0000.653-0.127-0.129-0.1300.399
avg_ticket_purc-0.0510.773-0.1170.6630.6520.111-0.0790.1120.8590.6531.0000.1200.1220.1220.770
returns-0.2590.400-0.2930.4190.2440.504-0.4650.2840.141-0.1270.1201.0000.9850.9870.376
volume_products_ret-0.2610.391-0.2810.4140.2320.489-0.4530.2720.147-0.1290.1220.9851.0000.9940.363
revenue_ret-0.2590.391-0.2800.4080.2310.489-0.4530.2720.139-0.1300.1220.9870.9941.0000.362
revenue_real-0.1810.994-0.4450.9260.8060.654-0.5720.4220.7280.3990.7700.3760.3630.3621.000

Missing values

2023-05-15T17:46:40.297687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-15T17:46:40.992287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idrevenue_purcrecency_daysvolume_products_purcassort_products_purcpurchasesavg_period_purcfrequency_purcvolume_basket_size_purcassort_basket_size_purcavg_ticket_purcreturnsvolume_products_retrevenue_retrevenue_real
0178505391.210372.0001733.00021.00034.0001.0000.09150.9710.618158.5651.00040.000102.5805288.630
1130473232.59056.0001390.000105.0009.00052.8330.024154.44411.667359.1777.00035.000143.4903089.100
2125836705.3802.0005028.000114.00015.00026.5000.040335.2007.600447.0252.00050.00076.0406629.340
313748948.25095.000439.00024.0005.00092.6670.01387.8004.800189.6500.0000.0000.000948.250
415100876.000333.00080.0001.0003.00020.0000.00826.6670.333292.0003.00022.000240.900635.100
5152914623.30025.0002102.00061.00014.00026.7690.037150.1434.357330.2365.00029.00071.7904551.510
6146885630.8707.0003621.000148.00021.00019.2630.056172.4297.048268.1376.000399.000523.4905107.380
7178095411.91016.0002057.00046.00012.00039.6670.032171.4173.833450.9932.00041.00067.0605344.850
81531160767.9000.00038194.000567.00091.0004.1910.243419.7146.231667.77927.000474.0001348.56059419.340
9160982005.63087.000613.00034.0007.00047.6670.01987.5714.857286.5190.0000.0000.0002005.630
customer_idrevenue_purcrecency_daysvolume_products_purcassort_products_purcpurchasesavg_period_purcfrequency_purcvolume_basket_size_purcassort_basket_size_purcavg_ticket_purcreturnsvolume_products_retrevenue_retrevenue_real
5752227004839.4201.0001074.00055.0001.000400.0000.5001074.00055.0004839.4200.0000.0000.0004839.420
575313298360.0001.00096.0002.0001.000400.0000.50096.0002.000360.0000.0000.0000.000360.000
575414569227.3901.00079.00010.0001.000400.0000.50079.00010.000227.3900.0000.0000.000227.390
57552270417.9001.00014.0007.0001.000400.0000.50014.0007.00017.9000.0000.0000.00017.900
5756227053.3501.0002.0002.0001.000400.0000.5002.0002.0003.3500.0000.0000.0003.350
5757227065699.0001.0001747.000634.0001.000400.0000.5001747.000634.0005699.0000.0000.0000.0005699.000
5758227076756.0600.0002010.000730.0001.000400.0001.0002010.000730.0006756.0600.0000.0000.0006756.060
5759227083217.2000.000654.00056.0001.000400.0001.000654.00056.0003217.2000.0000.0000.0003217.200
5760227093950.7200.000731.000217.0001.000400.0001.000731.000217.0003950.7200.0000.0000.0003950.720
576112713794.5500.000505.00037.0001.000400.0001.000505.00037.000794.5500.0000.0000.000794.550